CN103399893B - Method for retrieving objects on basis of hierarchical perception - Google Patents

Abstract

The invention relates to a target retrieval method based on layered perception. First, match the target to be retrieved in a single frame image of the video to be retrieved: by arranging a "search particle swarm" to find a similarity measure with the target to be retrieved that is higher than a certain value The area where these particles are located is the approximate location of the target, realizing low-resolution target perception; by arranging "focused particle swarms" to perform multi-scale and multi-neighborhood particle similarity matching, the precise matching position of the target is obtained. Then, the matching results of multiple frames of images in the retrieved video are integrated to determine whether the target to be retrieved appears in the retrieved video. The present invention can realize the positioning of the retrieved target in the video clip or single frame image, can effectively eliminate the inaccurate target positioning caused by the viewing angle, deformation, scale, color distribution, etc. , starting from the first frame of the video for object retrieval and localization.

Description

A Object Retrieval Method Based on Hierarchical Perception

technical field

The invention relates to the field of image pattern recognition, in particular to an object retrieval method based on layered perception.

Background technique

Content-based target retrieval technology refers to analyzing the content of images (or videos) by computer, and automatically extracting various feature information in images (or videos), including color, texture, space, etc., after similarity discrimination and reasoning, Finally, similar targets are retrieved. As the amount of image and video information continues to increase, the research on this content-based target retrieval technology has great theoretical value and broad application prospects. The key technology lies in the extraction and matching of target feature information.

The feature matching of the target is to measure the similarity of the feature values between the targets. At present, the feature matching can be roughly divided into two types: exact matching and similarity matching. The so-called complete matching refers to the exact matching of the target feature values, and the two are exactly the same; the similarity matching means that when the target feature values meet certain conditions, the two targets are considered to be the same. Due to the differences in technical means, the feature values of similar objects cannot be exactly the same. Therefore, similarity matching is one of the main methods of object retrieval at present. How to eliminate the angle of view, deformation, scale, The difference in color distribution and other aspects is an important issue that needs to be considered, and it is also one of the main reasons for the differences in the effects of various target retrieval methods.

A good object retrieval method should conform to the perception and judgment of the human visual system. The human visual system perceives targets in layers, first is low-resolution target search, and second is high-resolution target positioning to achieve deep understanding and perception of targets.

Contents of the invention

The technical solution of the present invention is to overcome the deficiencies of the prior art, and provide a target retrieval method based on layered perception, and achieve the purpose of improving target positioning and retrieval accuracy by simulating the perception and reasoning mechanism of the human visual system.

The technical solution of the present invention: a target retrieval method based on layered perception. The target retrieval in the present invention refers to finding the target to be retrieved in the retrieved video after the target image to be retrieved is given, and calculating the target in the video image. exact location in . In order to achieve the above purpose, the present invention adopts a layered perception strategy, treats the target search process as the whitening process of the gray system, and provides a visual qualitative description of the matching degree. The implementation steps are as follows:

Step (1) After normalizing the target to be retrieved, describe it with a histogram distribution;

Step (2) Construct the spatial closure ellipse region of the retrieved video single frame image, which is defined as the search particle group, use the histogram distribution to describe the particles, and find the particles whose similarity measure with the target to be retrieved is higher than a certain value. The area is the approximate location where the target may be, and realizes low-resolution target perception;

Step (3) Randomly generate 2W multi-scale, multi-neighborhood elliptical arrowhead regions near the approximate position of the above target, define them as focused particle groups, use histogram distribution to describe the particles, and perform multi-scale, multi-neighborhood particle similarity Matching, to obtain the precise matching position of the target in the single frame image of the retrieved video, to achieve high-resolution target positioning;

Step (4) Synthesize the matching results of multiple frames of images in the retrieved video, determine whether the target to be retrieved appears in the retrieved video, and provide a visual gray qualitative description map of the matching degree.

The concrete realization of described step (1) is:

Step (1.1) scale down the target to be detected to a width of 80 pixels and a height of 152 pixels, and regard it as a particle s'=(x',y',H _x ',H _y ')=(40,76, 40,76), the particle is defined as follows: particle s=(x, y, H _x , H _y ) is an ellipse descriptor of the possible coverage of the target, where (x, y) is the center coordinate of the ellipse O _s , (H _x , H _y ) are the semi-major and semi-minor lengths of the ellipse, respectively.

Step (1.2) characterizes the particle s' with a histogram, and the distribution of the particle histogram is defined as follows: In the present invention, the size of the 8 × 8 × 4 histogram in the HSV space is used to describe the particle s in the elliptical region O _s Image features, the histogram is determined by the function h( _xi )=j, _xi ∈O _s , j∈{1,2,...,256}, the color distribution of pixels in O _s p _y ={p _y ^{( v)} } _v=1,...,m is determined by the following formula:

${{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; the\; y</span>}}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; v</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; f</span>}\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; I</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; (</span>\frac{<span\; class="notranslate">\; |</span><span\; class="notranslate">\; |</span>\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; |</span><span\; class="notranslate">\; |</span>}{\mathrm{<span\; class="notranslate">\; a</span>}}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; \&delta;</span>}<span\; class="notranslate">\; \&lsqb;</span>\mathrm{<span\; class="notranslate">\; h</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; v</span>}<span\; class="notranslate">\; \&rsqb;</span><span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; r</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\left\{\begin{array}{cc}\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; r</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}& \mathrm{<span\; class="notranslate">\; r</span>}<span\; class="notranslate">\; <</span>\mathrm{<span\; class="notranslate">\; 1</span>}\\ \mathrm{<span\; class="notranslate">\; 0</span>}& \mathrm{<span\; class="notranslate">\; o</span>}\mathrm{<span\; class="notranslate">\; t</span>}\mathrm{<span\; class="notranslate">\; h</span>}\mathrm{<span\; class="notranslate">\; e</span>}\mathrm{<span\; class="notranslate">\; r</span>}\end{array}\right.<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>$

Among them, I is the number of pixels in O _s , δ is the Kronecker delta function, and the parameter normalization factor To ensure that the m=8×8×4=256.

Described step (2) concrete realization steps are as follows:

Step (2.1) lay out the search particle swarm, and the search particle swarm is arranged as follows: uniformly select N ₁ points in the image P _i of the i-th frame as the "basic particle swarm" S _i ¹ ={s _ij ¹ ,j=1, ..., N ₁ }, the particle major axis semi-length H _x is equal, and the minor axis semi-length H _y is equal, and H _x and H _y take the minimum value that satisfies the following two conditions: (a) the ellipse where the particle is located arrowhead is the closure of P _i , which satisfies (b) The coincidence degree of particles in pairs is not less than 10%, that is Then set the inverted particle group S _i ² of the particle group S _i ¹ in P _i ={s _ij ² ,j=1,...,N ₁ }, so that the centers of s _ij ² and s _ij ¹ coincide, and s _ij ² The major axis semi-length H _x ' of s _ij ¹ is equal to the minor axis semi-length H _y , and the minor axis semi-length H _y ' of s _ij ² is equal to the major axis semi-length H _x of s _ij ¹ , S _i ¹ ∪ S _i ² Formed a search particle swarm;

Step (2.2) In the search particle swarm, by calculating the similarity measure between the two particle distributions, look for particles whose similarity measure with the target to be retrieved is higher than a certain set value, the two particle distributions p={p ^{( v)} } _v=1,...,m and q={q ^(v) } _v=1,...,m The similarity measure is the Bhattacharyya coefficient as follows:

$\mathrm{<span\; class="notranslate">\; \&rho;</span>}<span\; class="notranslate">\; \&lsqb;</span>\mathrm{<span\; class="notranslate">\; p</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; q</span>}<span\; class="notranslate">\; \&rsqb;</span><span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; v</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; m</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}\sqrt{{\mathrm{<span\; class="notranslate">\; p</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; v</span>}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; q</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; v</span>}<span\; class="notranslate">\; )</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>$

Step (2.3) records the particle information whose similarity is higher than the set value. The area where these particles are located is the approximate position where the target may be. The union of these areas is the search range to be considered in step (4). Other areas are no longer search.

In the step (3), by arranging a "focused particle swarm" at the approximate position of the target, multi-scale and multi-neighborhood particle similarity matching is performed to achieve high-resolution target positioning and obtain the single frame of the target in the video to be retrieved. Exact match location in the image. The specific implementation steps are as follows:

Particles in step (3.1) "focused particle swarm" are multi-scale and multi-neighborhood extensions of search particles that meet the conditions of step (3), and the focused particle swarm is determined as follows: For particle j in the search particle swarm (denoted as s _j ＝(x _j ,y _j ,H _x,j ,H _y,j )), according to the following steps to generate the focused particle group S _i,j ³ ∪S _i,j ⁴ :

Step (3.1.1) Randomly generate W non-repeating random numbers {α _k } _{k=1,..., W} and W non-repeating values in [0.1, 0.3] ] random number {β _k } _k=1,...,W , then in:

s _j,k ¹ ={x _j ×(1-β _k ),y _j ×(1-β _k ),H _x,j ×α _k ,H _y,j ×α _k },

s _j,k ² ＝{x _j ×(1-β _k ),y _j ×(1+β _k ),H _x,j ×α _k ,H _y,j ×α _k },

s _j,k ³ ＝{x _j ×(1+β _k ),y _j ×(1-β _k ),H _x,j ×α _k ,H _y,j ×α _k },

s _j,k ⁴ ＝{x _j ×(1+β _k ),y _j ×(1+β _k ),H _x,j ×α _k ,H _y,j ×α _k },

s _j,k ⁵ ={x _j ,y _j ,H _x,j ×α _k ,H _y,j ×α _k };

Step (3.1.2) Randomly generate W non-repeating random numbers {α _k } _{k=1,..., W} and W non-repeating values in [0.1, 0.3] ] random number {β _k } _k=1,...,W , then in:

s _j,k ¹ ={x _j ×(1-β _k ),y _j ×(1-β _k ),H _x,j ×α _k ,H _y,j ×α _k },

s _j,k ² ＝{x _j ×(1-β _k ),y _j ×(1+β _k ),H _x,j ×α _k ,H _y,j ×α _k },

s _j,k ³ ＝{x _j ×(1+β _k ),y _j ×(1-β _k ),H _x,j ×α _k ,H _y,j ×α _k },

s _j,k ⁴ ＝{x _j ×(1+β _k ),y _j ×(1+β _k ),H _x,j ×α _k ,H _y,j ×α _k },

s _j,k ⁵ ={x _j ,y _j ,H _x,j ×α _k ,H _y,j ×α _k }.

Step (3.2) Calculate the similarity measure between the particles in the focused particle swarm and the target particle with retrieval, and perform qualitative mapping according to the measured value. The average value of the positions of all focused particles whose mapped value meets certain conditions is the single frame image of the video to be retrieved. The precise matching position in , and the object localization in a single frame image has been realized since then. The similarity measure of particles is the matching degree of the image in the particle ellipse region. Human perception of the matching degree is qualitative. The qualitative mapping is defined as follows:

Said step (4) synthesizes the matching results of multiple frames of images in the retrieved video, determines whether the target to be retrieved appears in the retrieved video, and provides a visual qualitative description of the matching degree. The specific implementation steps are as follows:

Step (4.1) Arrange the matching frame numbers in sequence in the M frames of the retrieved video Dest. If there is a situation where consecutive T+ ₀ frames are matched, it is determined that the target to be retrieved appears in the retrieved video, and the appearance time is marked by the first and last frame numbers of the continuous matching frame sequence; otherwise, it is determined that the target to be retrieved does not appear in the retrieved video.

Step (4.2) provides a visual qualitative description map of the matching degree, which is used to visually display the local similarity of the target in the retrieved image G. The visual qualitative description map of the matching degree refers to a grayscale image defined by the following formula:

{(x,y,f(x,y))|x∈[0,width],y∈[0,height]} (4)

Among them, f(x, y) is the qualitative whitening weight function, and width and height are the width and height of the image respectively. Here, the position information of the target to be retrieved in the retrieved image G is expressed as the following interval gray number quadruple:

${<span\; class="notranslate">\; \&CircleTimes;</span>}_{\mathrm{<span\; class="notranslate">\; G</span>}}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; (</span>{<span\; class="notranslate">\; \&CircleTimes;</span>}_{\mathrm{<span\; class="notranslate">\; x</span>}}<span\; class="notranslate">\; ,</span>{<span\; class="notranslate">\; \&CircleTimes;</span>}_{\mathrm{<span\; class="notranslate">\; the\; y</span>}}<span\; class="notranslate">\; ,</span>{<span\; class="notranslate">\; \&CircleTimes;</span>}_{{\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; x</span>}}}<span\; class="notranslate">\; ,</span>{<span\; class="notranslate">\; \&CircleTimes;</span>}_{{\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; the\; y</span>}}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span><span\; class="notranslate">\; (</span><span\; class="notranslate">\; \&lsqb;</span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; w</span>}\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; t</span>}\mathrm{<span\; class="notranslate">\; h</span>}<span\; class="notranslate">\; \&rsqb;</span><span\; class="notranslate">\; ,</span><span\; class="notranslate">\; \&lsqb;</span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; h</span>}\mathrm{<span\; class="notranslate">\; e</span>}\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; g</span>}\mathrm{<span\; class="notranslate">\; h</span>}\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; \&rsqb;</span><span\; class="notranslate">\; ,</span><span\; class="notranslate">\; \&lsqb;</span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; w</span>}\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; t</span>}\mathrm{<span\; class="notranslate">\; h</span>}<span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; \&rsqb;</span><span\; class="notranslate">\; ,</span><span\; class="notranslate">\; \&lsqb;</span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; h</span>}\mathrm{<span\; class="notranslate">\; e</span>}\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; g</span>}\mathrm{<span\; class="notranslate">\; h</span>}\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; \&rsqb;</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 5</span>}<span\; class="notranslate">\; )</span>$

in Corresponding to the gray number of the center abscissa x, center ordinate y, major axis half length H _x and short axis half length H _y of the matching object in G in turn. suppose Obey the independent multivariate Gaussian distribution of each variable, the particle swarm arranged in G is {( _xi ,y _i ,H _x,i ,H _y,i ),i=1,...,N}, the particle swarm and the The similarity measure value of retrieval target is {ρ _i ,i=1,...,N}, then define The whitening weight function of is as follows:

$\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; Q</span>}<span\; class="notranslate">\; (</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; ...</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; max</span>}}<span\; class="notranslate">\; (</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\sqrt{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&pi;</span>}}{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; x</span>}}}\mathrm{<span\; class="notranslate">\; exp</span>}<span\; class="notranslate">\; (</span><span\; class="notranslate">\; -</span>{<span\; class="notranslate">\; (</span>\frac{\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{\mathrm{<span\; class="notranslate">\; 2</span>}{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; x</span>}}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&times;</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\sqrt{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&pi;</span>}}{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; the\; y</span>}}}\mathrm{<span\; class="notranslate">\; exp</span>}<span\; class="notranslate">\; (</span><span\; class="notranslate">\; -</span>{<span\; class="notranslate">\; (</span>\frac{\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{\mathrm{<span\; class="notranslate">\; 2</span>}{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; the\; y</span>}}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 6</span>}<span\; class="notranslate">\; )</span>$

$\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\sqrt{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&pi;</span>}}{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; x</span>}}}<span\; class="notranslate">\; =</span>\sqrt{{\mathrm{<span\; class="notranslate">\; \&rho;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}<span\; class="notranslate">\; ,</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\sqrt{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&pi;</span>}}{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; the\; y</span>}}}<span\; class="notranslate">\; =</span>\sqrt{{\mathrm{<span\; class="notranslate">\; \&rho;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 7</span>}<span\; class="notranslate">\; )</span>$

It can be seen from the definition that the whitening weight function takes the value of a qualitative set.

Compared with the prior art, the present invention has the advantages of:

(1) The present invention can locate the target to be retrieved in a video segment or a single frame image, thereby realizing target retrieval in a video library or an image library;

(2) The present invention simulates the layered perception model of human vision, simulates the qualitative descriptive thinking of human beings to the gray system, and is a kind of exploration to the application of anthropomorphic models;

(3) The present invention can effectively eliminate inaccurate target positioning caused by viewing angle, deformation, scale, color distribution, etc.;

(4) The present invention can perform target retrieval and positioning from the first frame of the video without performing video target foreground/background detection.

Description of drawings

Fig. 1 is a flow chart diagram of the overall idea of the present invention;

Fig. 2 is target matching flowchart in the present invention;

Figure 3 is a schematic diagram of the target matching effect, where (a) is an image with a retrieval target, and (b) is a matching target image of a certain frame in the retrieved video.

detailed description

The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention. Accompanying drawing 1 is the flow chart of the overall thinking of the present invention, and accompanying drawing 2 is the flow chart of the target matching proposed by the present invention. Described a kind of target retrieval method based on hierarchical perception and gray qualitative reasoning comprises the following steps:

Step S101: Describe the target to be retrieved with histogram distribution, and calculate the histogram distribution of the target to be detected. For the target to be detected Target obtained by manual selection or target detection algorithm, calculate its HSV color histogram distribution q={q ^(v) } _v=1,...,m according to (1) formula;

Step S102: Layout the "search particle swarm", and calculate the histogram distribution of the single-frame image "search particle swarm" in the retrieved video. Assuming that the retrieved video Dest has a total of M frames of images, in each frame of image P _i (i=1,...,M), generate a "search particle swarm" S _i ¹ ∪ S _i ² as follows: Evenly select N ₁ points in the i-th frame image P _i of the detection target Target as the center of the "basic particle group" S _i ¹ ={s _ij ¹ ,j=1,...,N ₁ }, the long axis of the particle is half The lengths H _x and H _y are equal, and H _x and H _y take the minimum value that satisfies the following two conditions: (1) The ellipse where the particle is located is the closure of P _i , which satisfies The coincidence degree of particles in pairs is not less than 10%, that is Then set the inverted particle group S _i ² of the particle group S _i ¹ in P _i ={s _ij ² ,j=1,...,N ₁ }, so that the centers of s _ij ² and s _ij ¹ coincide, and s _ij ² The major axis semi-length H _x ' of s _ij ¹ is equal to the minor axis semi-length H _y , and the minor axis semi-length H _y ' of s _ij ² is equal to the major axis semi-length H _x of s _ij ¹ , S _i ¹ ∪ S _i ² A search particle swarm is formed. For each particle in S _i ¹ ∪S _i ² , calculate its HSV color histogram distribution p _i,j ={p _i,j ^(v) } _v=1,...,m , i=1,...,M,j=1,...,2×N ₁ ;

Step S103: Screen the "search particle swarm" to obtain the approximate location of the target. Calculate the histogram distribution similarity value between Target and each particle in S _i ¹ ∪ S _i ² according to formula (2), and calculate the measurement value {ρ[p _i,j ,q],j=1,... .,2×N ₁ }'s mean value μ _i,1 and variance σ _i,1 , the set of all js satisfying ρ[p _i,j ,q]>ρ _i,1 +σ _i,1 is sorted by similarity After sorting from large to small, it is recorded as B _i,1 ;

Step S104: Layout the "focused particle swarm", and calculate the histogram distribution of the single-frame image "focused particle swarm" in the retrieved video. For particle j in B _i,1 (denoted as s _j =(x _j ,y _j ,H _x,j ,H _y,j )), generate “focused particle swarm” S _i,j ³ as follows ∪S _i,j ⁴ :

(1) Randomly generate W non-repeated random numbers {α _k } _k=1,...,W and W non-repeated random numbers with values in [0.1,0.3] Number {β _k } _k=1,...,W , then in:

s _j,k ¹ ={x _j ×(1-β _k ),y _j ×(1-β _k ),H _x,j ×α _k ,H _y,j ×α _k },

s _j,k ² ＝{x _j ×(1-β _k ),y _j ×(1+β _k ),H _x,j ×α _k ,H _y,j ×α _k },

s _j,k ³ ＝{x _j ×(1+β _k ),y _j ×(1-β _k ),H _x,j ×α _k ,H _y,j ×α _k },

s _j,k ⁴ ＝{x _j ×(1+β _k ),y _j ×(1+β _k ),H _x,j ×α _k ,H _y,j ×α _k },

s _j,k ⁵ ={x _j ,y _j ,H _x,j ×α _k ,H _y,j ×α _k };

(2) Randomly generate W non-repeating random numbers {α _k } _{k=1,..., W} and W non-repeating random numbers in [0.1, 0.3] Number {β _k } _k=1,...,W , then in:

s _j,k ¹ ={x _j ×(1-β _k ),y _j ×(1-β _k ),H _x,j ×α _k ,H _y,j ×α _k },

s _j,k ² ＝{x _j ×(1-β _k ),y _j ×(1+β _k ),H _x,j ×α _k ,H _y,j ×α _k },

s _j,k ³ ＝{x _j ×(1+β _k ),y _j ×(1-β _k ),H _x,j ×α _k ,H _y,j ×α _k },

s _j,k ⁴ ＝{x _j ×(1+β _k ),y _j ×(1+β _k ),H _x,j ×α _k ,H _y,j ×α _k },

s _j,k ⁵ ={x _j ,y _j ,H _x,j ×α _k ,H _y,j ×α _k }.

For each particle in S _i,j ³ ∪S _i,j ⁴ , calculate its HSV color histogram distribution p _i,j,k ＝{p _i,j,k ^(v) } _{v＝ 1,...,m} ,i=1,...,M,k=1,...,10×W;

Step S105: Screening "Focused Particle Swarms" for reasoning. For particle j in B _i,1 , calculate the histogram distribution similarity measurement value between Target and each particle in S _i,j ³ ∪ S i, _j ⁴ according to formula (2), and obtain the measurement value set D _{i, j} ＝{ρ[p(s),q]|s∈S _i,j ³ ∪S _i,j ⁴ }, calculate the value of D _i,j according to formula (3), and decide the matching situation according to the following reasoning rules:" Match if there exists ρ∈D _i,j such that Q(ρ)=0, and s ₀ =mean{s|s∈S _i,j ³ ∪S _i,j ⁴ ,Q(ρ[p(s), q])=0}, go to step S107; otherwise, if no matching position is found for all j, go to step S106"; wherein, s ₀ =(x ₀ ,y ₀ ,H _x,0 ,H _y,0 ) is the matching target position, and mean means taking the mean value, which is determined by the following formula: mean{s|s∈S _i,j ³ ∪S _i,j ⁴ }＝{(mean(x),mean(y),mean( H _x ),mean(H _y ))|(x,y,H _x ,H _y )∈S _i,j ³ ∪S _i,j ⁴ };

Step S106: determine that there is no image area matching the target to be detected in the current frame image, and turn to step S108;

Step S107: Determine that there is an image area matching the target to be detected in the current frame image, and this area is marked by s ₀ =(x ₀ ,y ₀ ,H _x,0 ,H _y,0 ), realizing high-resolution single Frame image target positioning, turn to step S108;

Step S108: Synthesize the matching results of multiple frames of images in the retrieved video, determine whether the target to be retrieved appears in the retrieved video, and provide a visual qualitative description map of the matching degree. In the M frame images of the retrieved video Dest, the matching frame numbers are arranged sequentially. If there is a situation where consecutive T+ ₀ frames are matched, it is determined that the target to be retrieved appears in the retrieved video, and the appearance time is marked by the first and last frame numbers of the continuous matching frame sequence; otherwise, it is determined that the target to be retrieved does not appear in the retrieved video. For each frame of image, a qualitative description map of the matching degree can be made by formula (4), which is used to visualize and intuitively display the local similarity of the target in the retrieved image, and the particle swarm used to calculate the qualitative description map consists of steps S102 to Composition of all particles used in S107.

In this embodiment, W=3.

As shown in Figure 3, the area marked by the ellipse in (a) is the target to be retrieved, and the area marked by the ellipse in (b) is the matching target in the "a certain frame of the retrieved image" calculated by the method of this embodiment. Accurate matching of the target is achieved in the case of being partially occluded.

Parts not described in detail in the present invention belong to the known techniques of those skilled in the art.

The above is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person familiar with the technical field can easily conceive of changes or changes within the technical scope disclosed in the present invention. Replacement should be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention should be determined by the protection scope of the claims.

Claims (4)

1. A target retrieval method based on layered perception, characterized in that the realization steps are as follows: Step (1) After normalizing the target to be retrieved, describe it with a histogram distribution; Step (2) Construct the spatial closure ellipse region of the retrieved video single frame image, which is defined as the search particle group, use the histogram distribution to describe the particles, and find the particles whose similarity measure with the target to be retrieved is higher than a certain value. The area is the approximate location where the target may be, and realizes low-resolution target perception; Step (3) Randomly generate 2W multi-scale, multi-neighborhood elliptical arrowhead regions near the approximate position of the above-mentioned target, which are defined as focused particle swarms, describe the particles with histogram distribution, and perform multi-scale, multi-neighborhood particle similarity Matching, to obtain the precise matching position of the target in the single frame image of the retrieved video, to achieve high-resolution target positioning; Step (4) Synthesize the matching results of multiple frames of images in the retrieved video, determine whether the target to be retrieved appears in the retrieved video, and provide a visual gray qualitative description map of the matching degree; Described step (2) concrete realization steps are as follows: Step (2.1) lay out the search particle swarm, and the search particle swarm is arranged as follows: uniformly select N ₁ points in the image P _i of the i-th frame as the "basic particle swarm" S _i ¹ ={s _ij ¹ ,j=1, ..., N ₁ }, the particle major axis semi-length H _x is equal, and the minor axis semi-length H _y is equal, and H _x and H _y take the minimum value that satisfies the following two conditions: (a) the ellipse where the particle is located arrowhead is the closure of P _i , which satisfies (b) The coincidence degree of particles in pairs is not less than 10%, that is j≠k; then set the inverted particle group S _i ² of the particle group S _i ¹ in P _i ={s _ij ² ,j=1,...,N ₁ }, so that the centers of s _ij ² and s _ij ¹ coincide , the major axis semi-length H _x ' of s _ij ² is equal to the minor axis semi-length H _y of s _ij ¹ , the minor axis semi-length H _y ' of s _ij ² is equal to the major axis semi-length H _x of s _ij ¹ , S _i ¹ ∪S _i ² constitutes the search particle swarm; Step (2.2) In the search particle swarm, by calculating the similarity measure between the two particle distributions, look for particles whose similarity measure with the target to be retrieved is higher than a certain set value, the two particle distributions p={p ^{( v)} } _v=1,...,m and q={q ^(v) } _v=1,...,m The similarity measure is the Bhattacharyya coefficient as follows: $\mathrm{<span\; class="notranslate">\; \&rho;</span>}<span\; class="notranslate">\; \&lsqb;</span>\mathrm{<span\; class="notranslate">\; p</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; q</span>}<span\; class="notranslate">\; \&rsqb;</span><span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; v</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; m</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}\sqrt{{\mathrm{<span\; class="notranslate">\; p</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; v</span>}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; q</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; v</span>}<span\; class="notranslate">\; )</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>$ Step (2.3) records the information of the particles whose similarity is higher than the set value. The area where these particles are located is the approximate position where the target may be. The union of these areas is the search range to be considered in step (3), and other areas are no longer carried out. search. 2. the target retrieval method based on layered perception according to claim 1, is characterized in that: the concrete realization of described step (1) is: Step (1.1) scale down the target to be detected to a width of 80 pixels and a height of 152 pixels, and treat it as a particle The particle is defined as follows: Particle s=(x, y, H _x , H _y ) is an ellipse descriptor of the possible coverage of the target, where (x, y) is the center coordinate of the ellipse O _s , (H _x , H _y ) are the major axis semi-length and the minor axis semi-length of the ellipse respectively; Step (1.2) Use a histogram to characterize the particle s _o , and the distribution of the particle histogram is defined as follows: use a histogram with a size of 8×8×4 in the HSV space to describe the image characteristics of the elliptical region O _s where the particle s is located, The histogram is determined by the function h( _xi )=j, _xi ∈O _s , j∈{1,2,...,256}, the color distribution of pixels in O _s p _y ={p _y ^(v) } _v=1,...,m is determined by the following formula: ${{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; the\; y</span>}}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; v</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; f</span>}\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; I</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; (</span>\frac{<span\; class="notranslate">\; |</span><span\; class="notranslate">\; |</span>\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; |</span><span\; class="notranslate">\; |</span>}{\mathrm{<span\; class="notranslate">\; a</span>}}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; \&delta;</span>}<span\; class="notranslate">\; \&lsqb;</span>\mathrm{<span\; class="notranslate">\; h</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; v</span>}<span\; class="notranslate">\; \&rsqb;</span><span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; r</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\left\{\begin{array}{cc}\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; r</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}& \mathrm{<span\; class="notranslate">\; r</span>}<span\; class="notranslate">\; <</span>\mathrm{<span\; class="notranslate">\; 1</span>}\\ \mathrm{<span\; class="notranslate">\; 0</span>}& \mathrm{<span\; class="notranslate">\; o</span>}\mathrm{<span\; class="notranslate">\; t</span>}\mathrm{<span\; class="notranslate">\; h</span>}\mathrm{<span\; class="notranslate">\; e</span>}\mathrm{<span\; class="notranslate">\; r</span>}\end{array}\right.<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>$ Among them, I is the number of pixels in O _s , δ is the Kronecker delta function, and the parameter normalization factor To ensure that the m=8×8×4=256. 3. The object retrieval method based on layered perception according to claim 1, characterized in that: said step (3) carries out multi-scale, multi-neighborhood particle similarity by arranging focused particle swarms at the approximate position of the above-mentioned object Degree matching, to achieve high-resolution target positioning, to obtain the precise matching position of the target in the single frame image of the video to be retrieved, the specific implementation steps are as follows: The particles in the focused particle swarm in step (3.1) are the multi-scale and multi-neighborhood extensions of the search particles that meet the conditions of step (2), and the focused particle swarm is determined as follows: For particle j in the search particle swarm (denoted as s _j =( x _j ,y _j ,H _x,j ,H _y,j )), according to the following steps to generate the focused particle swarm S _i,j ³ ∪S _i,j ⁴ : Step (3.1.1) Randomly generate W non-repeating random numbers {α _k } _{k=1,..., W} and W non-repeating values in [0.1, 0.3] ] random number {β _k } _k=1,...,W , then in: s _j,k ¹ ={x _j ×(1-β _k ),y _j ×(1-β _k ),H _x,j ×α _k ,H _y,j ×α _k }, s _j,k ² ＝{x _j ×(1-β _k ),y _j ×(1+β _k ),H _x,j ×α _k ,H _y,j ×α _k }, s _j,k ³ ＝{x _j ×(1+β _k ),y _j ×(1-β _k ),H _x,j ×α _k ,H _y,j ×α _k }, s _j,k ⁴ ＝{x _j ×(1+β _k ),y _j ×(1+β _k ),H _x,j ×α _k ,H _y,j ×α _k }, s _j,k ⁵ ={x _j ,y _j ,H _x,j ×α _k ,H _y,j ×α _k }; Step (3.1.2) Randomly generate W non-repeating random numbers {α _k } _{k=1,..., W} and W non-repeating values in [0.1, 0.3] ] random number {β _k } _k=1,...,W , then in: s _j,k ¹ ={x _j ×(1-β _k ),y _j ×(1-β _k ),H _x,j ×α _k ,H _y,j ×α _k }, s _j,k ² ＝{x _j ×(1-β _k ),y _j ×(1+β _k ),H _x,j ×α _k ,H _y,j ×α _k }, s _j,k ³ ＝{x _j ×(1+β _k ),y _j ×(1-β _k ),H _x,j ×α _k ,H _y,j ×α _k }, s _j,k ⁴ ＝{x _j ×(1+β _k ),y _j ×(1+β _k ),H _x,j ×α _k ,H _y,j ×α _k }, s _j,k ⁵ ={x _j ,y _j ,H _x,j ×α _k ,H _y,j ×α _k }; Step (3.2) Calculate the similarity measure between the particles in the focused particle swarm and the target particle with retrieval, and perform qualitative mapping according to the measured value. The average value of the positions of all focused particles whose mapped value meets certain conditions is the single frame image of the video to be retrieved. The precise matching position in ; since then, the target positioning in a single frame image has been realized. The similarity measure of particles is the matching degree of the image in the particle ellipse area. Human perception of matching degree is qualitative, and the qualitative mapping is defined as follows: 4. The object retrieval method based on layered perception according to claim 1, characterized in that: said step (4) synthesizes the matching results of multiple frames of images in the retrieved video, and determines whether the object to be retrieved appears in the retrieved video , and give a visual qualitative description of the matching degree. The specific implementation steps are as follows: Step (4.1) In the M frame images of the retrieved video Dest, arrange the matching frame numbers in sequence. If there are consecutive T ₀ frames that match, it is determined that the target to be retrieved appears in the retrieved video, and the time of appearance is determined by consecutive Match the first and last frame number marks of the frame sequence; otherwise, it is determined that the target to be retrieved does not appear in the retrieved video; Step (4.2) provides a visual qualitative description map of the matching degree, which is used to visually display the local similarity of the target in the retrieved image G. The visual qualitative description map of the matching degree refers to a grayscale image defined by the following formula: {(x,y,f(x,y))|x∈[0,width],y∈[0,height]} (4) Among them, f(x, y) is a qualitative whitening weight function, and width and height are the width and height of the image respectively. Here, the position information of the target to be retrieved in the retrieved image G is expressed as the following interval gray number quadruple: ${<span\; class="notranslate">\; \&CircleTimes;</span>}_{\mathrm{<span\; class="notranslate">\; G</span>}}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; (</span>{<span\; class="notranslate">\; \&CircleTimes;</span>}_{\mathrm{<span\; class="notranslate">\; x</span>}}<span\; class="notranslate">\; ,</span>{<span\; class="notranslate">\; \&CircleTimes;</span>}_{\mathrm{<span\; class="notranslate">\; the\; y</span>}}<span\; class="notranslate">\; ,</span>{<span\; class="notranslate">\; \&CircleTimes;</span>}_{{\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; x</span>}}}<span\; class="notranslate">\; ,</span>{<span\; class="notranslate">\; \&CircleTimes;</span>}_{{\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; the\; y</span>}}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span><span\; class="notranslate">\; (</span><span\; class="notranslate">\; \&lsqb;</span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; w</span>}\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; t</span>}\mathrm{<span\; class="notranslate">\; h</span>}<span\; class="notranslate">\; \&rsqb;</span><span\; class="notranslate">\; ,</span><span\; class="notranslate">\; \&lsqb;</span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; h</span>}\mathrm{<span\; class="notranslate">\; e</span>}\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; g</span>}\mathrm{<span\; class="notranslate">\; h</span>}\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; \&rsqb;</span><span\; class="notranslate">\; ,</span><span\; class="notranslate">\; \&lsqb;</span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; w</span>}\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; t</span>}\mathrm{<span\; class="notranslate">\; h</span>}<span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; \&rsqb;</span><span\; class="notranslate">\; ,</span><span\; class="notranslate">\; \&lsqb;</span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; h</span>}\mathrm{<span\; class="notranslate">\; e</span>}\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; g</span>}\mathrm{<span\; class="notranslate">\; h</span>}\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; \&rsqb;</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 5</span>}<span\; class="notranslate">\; )</span>$ in In turn, it is the gray number of the center abscissa x, center y coordinate y, major axis semi-length H _x and short axis semi-length H _y of the matching object in G; assuming Obey the independent multivariate Gaussian distribution of each variable, the particle swarm arranged in G is {( _xi ,y _i ,H _x,i ,H _y,i ),i=1,...,N}, the particle swarm and the The similarity measure value of the retrieval target is {ρ _i ,i=1,...,N}, which means The whitening weight function of is as follows: $\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; Q</span>}<span\; class="notranslate">\; (</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; ,</span><span\; class="notranslate">\; ...</span><span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; m</span>}\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; x</span>}}<span\; class="notranslate">\; (</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\sqrt{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&pi;</span>}}{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; x</span>}}}\mathrm{<span\; class="notranslate">\; exp</span>}<span\; class="notranslate">\; (</span><span\; class="notranslate">\; -</span>{<span\; class="notranslate">\; (</span>\frac{\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{\mathrm{<span\; class="notranslate">\; 2</span>}{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; x</span>}}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&times;</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\sqrt{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&pi;</span>}}{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; the\; y</span>}}}\mathrm{<span\; class="notranslate">\; exp</span>}<span\; class="notranslate">\; (</span><span\; class="notranslate">\; -</span>{<span\; class="notranslate">\; (</span>\frac{\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{\mathrm{<span\; class="notranslate">\; 2</span>}{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; the\; y</span>}}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 6</span>}<span\; class="notranslate">\; )</span>$ $\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\sqrt{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&pi;</span>}}{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; x</span>}}}<span\; class="notranslate">\; =</span>\sqrt{{\mathrm{<span\; class="notranslate">\; \&rho;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}<span\; class="notranslate">\; ,</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\sqrt{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&pi;</span>}}{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; the\; y</span>}}}<span\; class="notranslate">\; =</span>\sqrt{{\mathrm{<span\; class="notranslate">\; \&rho;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 7</span>}<span\; class="notranslate">\; )</span>$ It can be seen from the above formula that the value of the whitening weight function is in the qualitative set.